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utils.py
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utils.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Utility functions for the ligand-iron-microbe box model analysis
"""
import numpy as np
import pandas as pd
def oceanmasks(xc,yc,maskin):
from scipy.interpolate import griddata
nzdim=0
# Find if input dimensions are 3d or 2d
if np.ndim(maskin)>2:
nzdim=np.size(maskin,2)
if np.ndim(xc)>2:
xc=xc[:,:,0]
if np.ndim(yc)>2:
yc=yc[:,:,0]
mask_file='woa13_basinmask_01.msk'
x = np.loadtxt(mask_file,delimiter=',',usecols=(1,),skiprows=2)
y = np.loadtxt(mask_file,delimiter=',',usecols=(0,),skiprows=2)
basinfile = np.loadtxt(mask_file,delimiter=',',usecols=(2,),skiprows=2)
# Find out if the grid has been rotated and rotate so range is the same as input grid
if (np.min(x)<0) != (np.min(xc)<0):
x[x<0]=x[x<0]+360
basinmask = griddata((x, y), basinfile, (xc,yc), method = 'nearest')
basinmask[basinmask==12]=2 # Add the Sea of Japan to the Pacific
basinmask[basinmask==56]=3 # Add Bay of Bengal to Indian Ocean
basinmask[basinmask==53]=0 # Zero out Caspian Sea
so_mask = np.copy(basinmask)
so_mask[so_mask!=10]=0
so_mask[so_mask==10]=1
arctic_mask = np.copy(basinmask)
arctic_mask[arctic_mask!=11]=0
arctic_mask[arctic_mask==11]=1
# Divide Southern Ocean into Atlantic, Indian and Pacific Sectors
tmp=basinmask[:,0:len(np.unique(yc[yc<=-45]))]
basinmask[:,0:np.size(tmp,1)]=np.transpose(np.tile(tmp[:,-1],[np.size(tmp,1),1]))
atlantic_mask = np.copy(basinmask)
atlantic_mask[atlantic_mask!=1]=0
atlantic_mask[atlantic_mask==1]=1
indian_mask = np.copy(basinmask)
indian_mask[indian_mask!=3]=0
indian_mask[indian_mask==3]=1
pacific_mask = np.copy(basinmask)
pacific_mask[pacific_mask!=2]=0
pacific_mask[pacific_mask==2]=1
# if input was 3d, then extent mask to 3d
if nzdim>0:
atlantic_mask = np.tile(atlantic_mask[:,:,np.newaxis],(1,1,nzdim))*maskin
pacific_mask = np.tile(pacific_mask [:,:,np.newaxis],(1,1,nzdim))*maskin
indian_mask = np.tile(indian_mask [:,:,np.newaxis],(1,1,nzdim))*maskin
so_mask = np.tile(so_mask [:,:,np.newaxis],(1,1,nzdim))*maskin
arctic_mask = np.tile(arctic_mask [:,:,np.newaxis],(1,1,nzdim))*maskin
return atlantic_mask, pacific_mask, indian_mask, so_mask, arctic_mask
def calc_cost(modin,ref,stdev,iters=1):
# Use the old way using masked arrays or ndarrays
if np.ndim(modin)<=1:
iters=1
else:
iters=np.max(np.shape(modin))
cost=np.sum(np.power(modin.transpose()-np.tile(ref,(iters,1)),2)/np.tile(np.power(stdev,2),(iters,1)),axis=1)
return cost
def calc_boxmodel_vars_iter(data_pd,area,ivar,nref,nstd,fref,fstd,lref,lstd,Rcp=106,Rnp=16):
# Calculate box model diagnostics - requires pandas input
global ncost, fcost, lcost, pstar, nsurfmean, fsurfmean, lsurfmean, \
nso, fso, lso, nna, fna, lna, ndo, fdo, ldo, nlimit, export, expbox
tlen=data_pd.shape[0]
# Calculate cost function
nc=calc_cost(np.array((data_pd.p1[:tlen]*Rnp ,data_pd.p2[:tlen]*Rnp ,data_pd.p3[:tlen]*Rnp)) ,nref,nstd)
fc=calc_cost(np.array((data_pd.f1[:tlen] ,data_pd.f2[:tlen] ,data_pd.f3[:tlen])) ,fref,fstd)
lc=calc_cost(np.array((data_pd.l1[:tlen] ,data_pd.l2[:tlen] ,data_pd.l3[:tlen])) ,lref,lstd)
# Store final cost function values
ncost[ivar]=nc[-1]
fcost[ivar]=fc[-1]
lcost[ivar]=lc[-1]
molpsm1_2_gtcym1=Rcp*86400*365*12*1e-15 # Convert mol P/s to GtC/yr
pstar[ivar]=data_pd.pstar.tail(1)
expbox[ivar,0] =data_pd.export1.tail(1).to_numpy(copy=True)*molpsm1_2_gtcym1
expbox[ivar,1] =data_pd.export2.tail(1).to_numpy(copy=True)*molpsm1_2_gtcym1
export[ivar] =(data_pd.export1.tail(1).to_numpy(copy=True)+data_pd.export2.tail(1).to_numpy(copy=True))*molpsm1_2_gtcym1
nlimit[ivar]=data_pd.lim.tail(1).to_numpy(copy=True)
nsm=np.array((data_pd.p1*area[0]+data_pd.p2*area[1])/(area[0]+area[1]))*Rnp
fsm=np.array((data_pd.f1*area[0]+data_pd.f2*area[1])/(area[0]+area[1]))
lsm=np.array((data_pd.l1*area[0]+data_pd.l2*area[1])/(area[0]+area[1]))
nsurfmean[ivar]=nsm[-1]
fsurfmean[ivar]=fsm[-1]
lsurfmean[ivar]=lsm[-1]
nso[ivar]=data_pd.p1.tail(1).to_numpy(copy=True)*Rnp
fso[ivar]=data_pd.f1.tail(1).to_numpy(copy=True)
lso[ivar]=data_pd.l1.tail(1).to_numpy(copy=True)
nna[ivar]=data_pd.p2.tail(1).to_numpy(copy=True)*Rnp
fna[ivar]=data_pd.f2.tail(1).to_numpy(copy=True)
lna[ivar]=data_pd.l2.tail(1).to_numpy(copy=True)
ndo[ivar]=data_pd.p3.tail(1).to_numpy(copy=True)*Rnp
fdo[ivar]=data_pd.f3.tail(1).to_numpy(copy=True)
ldo[ivar]=data_pd.l3.tail(1).to_numpy(copy=True)
def calc_boxmodel_vars(data_pd,df_aux,df_refs,Rcp=106):
# Calculate box model diagnostics - requires pandas input
data_pd['ncost']=calc_cost((data_pd[["nso","nna","ndo"]].T.values),
(df_refs.filter(regex='^n[a-zA-Z]{2}ref').values),
(df_refs.filter(regex='^n[a-zA-Z]{2}std').values))
data_pd['fcost']=calc_cost((data_pd[["fso","fna","fdo"]].T.values),
(df_refs.filter(regex='^f[a-zA-Z]{2}ref').values),
(df_refs.filter(regex='^f[a-zA-Z]{2}std').values))
data_pd['lcost']=calc_cost((data_pd[["lso","lna","ldo"]].T.values),
(df_refs.filter(regex='^l[a-zA-Z]{2}ref').values),
(df_refs.filter(regex='^l[a-zA-Z]{2}std').values))
data_pd['nsurf']=(data_pd["nso"]*df_aux["areaso"]+data_pd["nna"]*df_aux["areana"])/(df_aux["areaso"]+df_aux["areana"])
data_pd['fsurf']=(data_pd["fso"]*df_aux["areaso"]+data_pd["fna"]*df_aux["areana"])/(df_aux["areaso"]+df_aux["areana"])
data_pd['lsurf']=(data_pd["lso"]*df_aux["areaso"]+data_pd["lna"]*df_aux["areana"])/(df_aux["areaso"]+df_aux["areana"])
data_pd["exportt"]=(data_pd["export1"].values+data_pd["export2"].values)*Rcp*86400*365*12*1e-15 # convert molP/s to GtC/yr
# Model-data comparison score
# number of obs used in objective function - 3 boxes and 3 variables (weighted equally)
nobs=df_refs.filter(regex='^[a-zA-Z]{3}ref').count().count()
data_pd['jovern'] = np.exp(-1*(data_pd['ncost']+data_pd['fcost']+data_pd['lcost'])/nobs)
# Seperate contributions (multiply to recover full score)
data_pd['jovernn'] = np.exp(-1*(data_pd['ncost'])/nobs)
data_pd['jovernf'] = np.exp(-1*(data_pd['fcost'])/nobs)
data_pd['jovernl'] = np.exp(-1*(data_pd['lcost'])/nobs)
return data_pd
def run_boxmodel_iter(parray,farray,larray,gamma_fe,lt_rate,
dustdep,ventdep,alpha_yr,psi,dlambdadz,niters,ninit,
nref,nstd,fref,fstd,lref,lstd,area,Rcp=117,Rnp=16):
# nutboxmod provides "model" which is the fortran model compiled with "f2py"
import importlib.util
spec = importlib.util.spec_from_file_location("nutboxmod", "/Users/jml1/GitHub/Lauderdale_ligand_iron_microbe_feedback/nutboxmod.cpython-37m-darwin.so")
nutboxmod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(nutboxmod)
global ncost, fcost, lcost, pstar, nsurfmean, fsurfmean, lsurfmean, \
nso, fso, lso, nna, fna, lna, ndo, fdo, ldo, nlimit, export, expbox
# for ivar in prange(niters):
for ivar in range(niters):
if np.remainder(ivar,250)==0:
print("Running iteration "+np.str(ivar+1)+" out of "+np.str(niters))
# Need to run for slightly different periods of time for deep ocean equilibrium,
if np.logical_and((lt_rate[ivar]/3e7)>=(10**1.25),(gamma_fe[ivar]/106)<=(10**-4)):
# run the wider upper left corner for a hundred thousand years to equilibrium
maxyears = 1e5
outputyears = 1e2
else:
# run the rest of the domain for ten thousand years to equilibrium
maxyears = 1e4
outputyears = 10
# Convert umol/kg or nmol/kg to mol/kg
pin = parray[ivar]*1.0e-6
fin = farray[ivar]*1.0e-9
lin = larray[ivar]*1.0e-9
gin = gamma_fe[ivar]
lif = lt_rate[ivar]
tout,pout,fout,lout,epout,nlout,psout = nutboxmod.model(maxyears,outputyears,
pin,fin,lin,gin,lif,dustdep,ventdep,alpha_yr,dlambdadz,psi,ivar+ninit)
# Direct model output is a fixed length of outstepmax (=10000 lines), so is maxyears/outputyears is
# less than 10000 need to cut off the zeros from the end of the output arrays
tlen=np.int(maxyears/outputyears)
data_pd = pd.DataFrame(np.hstack((tout[:tlen,np.newaxis],pout[:,:tlen].T,fout[:,:tlen].T,lout[:,:tlen].T,epout[0:2,:tlen].T,nlout[:tlen,np.newaxis],psout[:tlen,np.newaxis])),
columns=["t","p1","p2","p3","f1","f2","f3","l1","l2","l3","export1","export2","lim","pstar"])
calc_boxmodel_vars_iter(data_pd,area,ivar,nref,nstd,fref,fstd,lref,lstd,Rcp=Rcp,Rnp=Rnp)
def run_boxmodel(df_input,df_parms,df_aux,df_refs,workers=1,Rcp=106,Rnp=16):
'''
Run the box model:
a) workers=1; in series using pandas' "apply" to run through rows of a data frame of inputs
b) workers>1; in parallel using pandarallel's "parallel_apply"
returns pandas dataframe with the results
'''
df_tim=df_parms[["gamma","lambda"]].rename(columns={"gamma":"nyrs","lambda":"tout"})
# Want to run some of these models a lot longer to reach equilibrium
df_tim["nyrs"].where(np.logical_and((df_parms["lambda"]/3e7)>(10**1),
(df_parms["gamma"]/Rcp)<(10**-4.75)),1.e4,inplace=True)
df_tim["nyrs"].mask (np.logical_and((df_parms["lambda"]/3e7)>=(10**1),
(df_parms["gamma"]/Rcp)<=(10**-4.75)),1.e5,inplace=True)
df_tim["tout"].where(np.logical_and((df_parms["lambda"]/3e7)>(10**1),
(df_parms["gamma"]/Rcp)<(10**-4.75)),10. ,inplace=True)
df_tim["tout"].mask (np.logical_and((df_parms["lambda"]/3e7)>=(10**1),
(df_parms["gamma"]/Rcp)<=(10**-4.75)),100.,inplace=True)
#df_tim["nyrs"] = df_tim["nyrs"]*0 + 1e4
#df_tim["tout"] = df_tim["tout"]*0 + 10
df_boxmodel=pd.concat((df_tim,df_input,df_parms,df_aux[["gamma_over_lambda"]]),axis=1,sort=False)
# Define private helper function
def _boxmodel(self):
# nutboxmod provides "model" which is the fortran model compiled with "f2py"
import glob as gb
import importlib.util as iu
spec = iu.spec_from_file_location("nutboxmod",gb.glob("nutboxmod*so")[0])
nutboxmod = iu.module_from_spec(spec)
spec.loader.exec_module(nutboxmod)
nt=self.nyrs
dt=self.tout
# Order or inputs:
# "nyrs","tout",
# "n1","n2","n3","f1","f2","f3","l1","l2","l3",
# "gamma","lambda","sFetop","sFebot","alphabio","dlambdadz","psi","niter"
timeseries = pd.DataFrame(
nutboxmod.model(
*self [["nyrs","tout",
"nso","nna","ndo","fso","fna","fdo","lso","lna","ldo",
"gamma","lambda","sFetop","sFebot","alphabio",
"dlambdadz","psi","niter"
]].values),
index=["tout","nso","nna","ndo","fso","fna","fdo","lso","lna","ldo",
"export1","export2","lim","pstar"]).transpose()
return pd.concat((self[["nyrs"]],timeseries.iloc[np.int(nt/dt)],
self[["gamma","lambda","gamma_over_lambda","sFetop","sFebot",
"alphabio","psi","dlambdadz","niter"]]))
if workers==1:
# Run in serial mode (avoids overhead of pandarallel if not running multiprocessor)
df_output=df_boxmodel.apply(_boxmodel,axis=1)
elif workers>1:
# Run in parallel
from pandarallel import pandarallel
pandarallel.initialize(nb_workers=workers,progress_bar=True)
df_output=df_boxmodel.parallel_apply(_boxmodel,axis=1)
# Boxmodel macronutrient is phosphate, do we need to convert to Nitrate?
df_output[["nso","nna","ndo"]]=df_output[["nso","nna","ndo"]]*Rnp
# Do some post processing
df_output=calc_boxmodel_vars(df_output,df_aux,df_refs,Rcp=Rcp)
return df_output
def read_boxmodel_iter(ninit,niters,nref,nstd,fref,fstd,lref,lstd,area,fprefix='ironmodel',Rcp=117,Rnp=16):
global ncost, fcost, lcost, pstar, nsurfmean, fsurfmean, lsurfmean, \
nso, fso, lso, nna, fna, lna, ndo, fdo, ldo, nlimit, export, expbox
# Read in the output files
for ivar in range(niters):
if np.remainder(ivar,250)==0:
print("Reading iteration "+np.str(ninit+ivar+1)+" out of "+np.str(niters))
fname=fprefix+np.str(("%06d" % (ninit+ivar,)))+'.dat'
data_pd = pd.read_csv(fname, delimiter="\s+",skiprows=0,header=0,
names=["t","p1","p2","p3","f1","f2","f3","l1","l2","l3","export1","export2","lim","pstar"])
calc_boxmodel_vars_iter(data_pd,area,ivar,nref,nstd,fref,fstd,lref,lstd,Rcp=Rcp,Rnp=Rnp)
def calc_gamma_over_lambda_range(expin=None,volin=None,ltin=None):
#This is a data-based estimate of gamma/lambda.
# data_goverl=np.log10(np.array((1/0.84e-5,1/1.82e-3)))
if expin is None:
# Global ocean production estimate,
# 20GtC from MITgcm (Lauderdale et al., 2016)
# 50GtC from Field et al., (1998)
expin=np.array((10,20,50,100)) # in GtC/y
if volin is None:
# Volume from here: https://www.ngdc.noaa.gov/mgg/global/etopo1_ocean_volumes.html
# ocvol[0] is volume of ML estimate, ocvol[1] is global ocean volume
volin=np.array((3.619e14*200,1.335e+18)) # in m3
if ltin is None:
# Ligand concentration estimates: 1nm Parekh; 2.5nm GEOTRACES average
ltin=np.array((0.6e-6,1e-6,2.5e-6,6e-6))
netexp,ocvol,ltest=np.meshgrid(expin*1e9*1e6*(1/12)*(1/(86400*365)), volin, ltin)
goverl=ltest/(netexp/ocvol)
return np.sort(np.log10(np.array((np.min(goverl),np.max(goverl))))), goverl
def mad(arr):
""" Median Absolute Deviation: a "Robust" version of standard deviation.
Indices variabililty of the sample.
https://en.wikipedia.org/wiki/Median_absolute_deviation
"""
arr = np.ma.array(arr).compressed() # should be faster to not use masked arrays.
med = np.median(arr)
return np.median(np.abs(arr - med))
# set macronutrient reference for the cost function from WOA13 annual climatology
def get_macro_reference(fname,Rnp=16):
from geopy.distance import geodesic as ge
import netCDF4 as nc
import numpy.ma as nm
try:
woa=nc.Dataset(fname,mode='r')
n_woa_lat=woa.dimensions['lat' ].size
n_woa_lon=woa.dimensions['lon' ].size
n_woa_dep=woa.dimensions['depth' ].size
# Get axes - these are cell centres
woa_lat=woa.variables['lat'][:]
woa_lon=woa.variables['lon'][:]
woa_dep=woa.variables['depth'][:]
# Get the nutrient data
if Rnp==16:
woan=np.squeeze(np.transpose(woa.variables['n_an'][:],(3,2,1,0)))
else:
woan=np.squeeze(np.transpose(woa.variables['p_an'][:],(3,2,1,0)))
# Reshape axes to have the same dimensions
woa_dep=np.tile(np.transpose(woa_dep[:,np.newaxis,np.newaxis],(2,1,0)),(n_woa_lon,n_woa_lat,1))
woa_lat=np.tile(np.transpose(woa_lat[:,np.newaxis,np.newaxis],(1,0,2)),(n_woa_lon,1,n_woa_dep))
woa_lon=np.tile(woa_lon[:,np.newaxis,np.newaxis],(1,n_woa_lat,n_woa_dep))
# make axes cell edges
woa_latg=np.append(woa.variables['lat'][:]-0.5,woa.variables['lat'][-1]+0.5)
woa_latg=np.tile(np.transpose(woa_latg[:,np.newaxis,np.newaxis],(1,0,2)),(n_woa_lon,1,n_woa_dep))
woa_long=np.append(woa.variables['lon'][:]-0.5,woa.variables['lon'][-1]+0.5)
woa_long=np.tile(woa_long[:,np.newaxis,np.newaxis],(1,n_woa_lat,n_woa_dep))
# Use geopy's VincentyDistance to calulate dx and dy...note it's lat then lon input
woa_dy=np.zeros((n_woa_lon,n_woa_lat,n_woa_dep))
for jj in range(n_woa_lat):
woa_dy[:,jj,:]=ge((woa_latg[0,jj,0],woa_lon[0,jj,0]),(woa_latg[0,jj+1,0],woa_lon[0,jj,0])).m
woa_dx=np.zeros((n_woa_lon,n_woa_lat,n_woa_dep))
for jj in range(n_woa_lat):
for ii in range(n_woa_lon):
woa_dx[ii,jj,:]=ge((woa_lat[ii,jj,0],woa_long[ii,jj,0]),(woa_lat[ii,jj,0],woa_long[ii+1,jj,0])).m
# Calulate dz
woa_dz=np.diff(np.append(woa_dep,np.ones((n_woa_lon,n_woa_lat,1))*5600.0,axis=2),axis=2)
# Calcualte volume
woa_vol=nm.masked_array(woa_dx*woa_dy*woa_dz)
woa_vol.mask=woan.mask
# Close the netcdf file
woa.close()
# Get basin masks
woa_mask=(~woan.mask).astype(int)
woa_atlantic_mask, woa_pacific_mask, woa_indian_mask, woa_so_mask, woa_arctic_mask = oceanmasks(woa_lon,woa_lat,woa_mask)
woa_glob_mask=woa_atlantic_mask+woa_pacific_mask+woa_indian_mask+woa_so_mask
woa_basin_mask=woa_atlantic_mask+woa_pacific_mask+woa_indian_mask-woa_so_mask
nref=np.ones((1,3))
nstd=np.ones((1,3))
# Southern Ocean Macronutrient
nref[0,0]=nm.sum(
nm.masked_where(woa_dep>50,
nm.masked_where(woa_so_mask==0,woan*woa_vol)))/nm.sum(
nm.masked_where(woa_dep>50,
nm.masked_where(woa_so_mask==0,woa_vol)))
nstd[0,0]=nm.std(nm.masked_where(woa_dep>50,nm.masked_where(woa_so_mask==0,woan)))
# Atlantic Macronutrient
nref[0,1]=nm.sum(
nm.masked_where(woa_dep>50,
nm.masked_where(woa_basin_mask==0,
nm.masked_where(woa_so_mask==1,woan*woa_vol))))/nm.sum(
nm.masked_where(woa_dep>50,
nm.masked_where(woa_basin_mask==0,
nm.masked_where(woa_so_mask==1,woa_vol))))
nstd[0,1]=nm.std(nm.masked_where(woa_dep>50,nm.masked_where(woa_basin_mask==0,nm.masked_where(woa_so_mask==1,woan))))
# Deep Ocean Macronutrient
nref[0,2]=nm.sum(
nm.masked_where(woa_glob_mask==0,
nm.masked_where(woa_dep<=50,woan*woa_vol)))/nm.sum(
nm.masked_where(woa_glob_mask==0,
nm.masked_where(woa_dep<=50,woa_vol)))
nstd[0,2]=nm.std(nm.masked_where(woa_glob_mask==0,nm.masked_where(woa_dep<=50,woan)))
except FileNotFoundError:
# Just use values from the paper
nref=np.array([[23.97361974, 2.95820125, 31.62080854]])
nstd=np.array([[ 3.68839076, 5.07325561, 11.49532704]])
return nref, nstd
def get_micro_reference(fname):
# set Fe and L reference for the cost function from GEOTRACES IDP 2017
import netCDF4 as nc
import numpy.ma as nm
try:
idp = nc.Dataset(fname, mode='r')
# Variables of interest
vars= {
'Cruise':'metavar1', # Cruise
'Press' :'var1', # Pressure
'Depth' :'var2', # Depth (m)
'Bottle':'var4', # Bottle number
'Bottle2':'var5', # BODC Bottle number?
'Firing':'var6', # Firing Sequence
'Theta' :'var7', # CTDTEMP (°C)
'Salt' :'var8', # CTDSAL
'OXY' :'var20', # Oxygen concentration (umol/kg)
'OQC' :'var20_QC', # OxygenQuality control flags
'PO4' :'var21', # Phosphate (umol/kg)
'PQC' :'var21_QC', # Phosphate Quality control flags
'SIT' :'var23', # Silicate (umol/kg)
'SIQC' :'var23_QC', # Silicate Quality control flags
'NO3' :'var24', # Nitrate (umol/kg)
'NQC' :'var24_QC', # Nitrate Quality control flags
'ALK' :'var30', # ALK (umol/kg)
'AQC' :'var30_QC', # ALK Quality control flags
'DIC' :'var31', # DIC (umol/kg)
'CQC' :'var31_QC', # DIC Quality control flags
'FeT' :'var73', # Fe (nmol/kg)
'FQC' :'var73_QC', # Fe Quality control flags
'L1Fe' :'var231', # L1-Fe Ligand (nmol/kg)
'L1QC' :'var231_QC',# L1-Fe Quality control flags
'L2Fe' :'var233', # L2-Fe Ligand (nmol/kg)
'L2QC' :'var233_QC',# L2-Fe Quality control flags
}
# size of arrays
nsamp =idp.dimensions['N_SAMPLES' ].size
nstat =idp.dimensions['N_STATIONS'].size
# nchar =idp.dimensions['STRING6'].size
# load variables
lon = np.transpose([idp.variables['longitude'][:] for _ in range(nsamp)])
lon = np.where(lon>180, lon-360, lon)
lat = np.transpose([idp.variables['latitude' ][:] for _ in range(nsamp)])
depth = idp.variables[vars['Depth']][:]/1000 # Convert to km
# umol= idp.variables[vars['PO4']].units
# nmol=idp.variables[vars['FeT']].units
#critdepth=np.zeros((nstat,nsamp)) # Going to ignore data points near the bottom
#for ii in range(nstat):
# if np.max(depth[ii,:]) > 0.5:
# critdepth[ii,:]=np.max(depth[ii,:])-0.5
# else:
# critdepth[ii,:]=np.max(depth[ii,:])
# load variables
idp_lon = np.transpose([idp.variables['longitude'][:] for _ in range(nsamp)])
idp_lon = np.where(idp_lon>180, idp_lon-360, idp_lon)
idp_lat = np.transpose([idp.variables['latitude' ][:] for _ in range(nsamp)])
idp_dep = idp.variables[vars['Depth']][:]
# umol= idp.variables[vars['PO4']].units
# nmol=idp.variables[vars['FeT']].units
# Quality control flags are:
# 1 Good: Passed documented required QC tests
# 2 Not evaluated, not available or unknown: Used for data when no QC test performed or the information on quality is not available
# 3 Questionable/suspect: Failed non-critical documented metric or subjective test(s)
# 4 Bad: Failed critical documented QC test(s) or as assigned by the data provider
# 9 Missing data: Used as place holder when data are missing
fqc = np.zeros((nstat,nsamp))
tmp = idp.variables[vars['FQC']][:]
for ii in range(nstat):
for jj in range(nsamp):
fqc[ii,jj]=np.double(tmp.data[ii,jj].tostring().decode("utf-8"))
#idp_fe = nm.masked_where(np.logical_or(fqc>2,depth>=critdepth),idp.variables[vars['FeT']][:])
idp_fe = nm.masked_where(fqc>2,idp.variables[vars['FeT']][:])
l1qc = np.zeros((nstat,nsamp))
tmp = idp.variables[vars['L1QC']][:]
for ii in range(nstat):
for jj in range(nsamp):
l1qc[ii,jj]=np.double(tmp.data[ii,jj].tostring().decode("utf-8"))
#idp_l1 = nm.masked_where(np.logical_or(l1qc>2,depth>=critdepth),idp.variables[vars['L1Fe']][:])
idp_l1 = nm.masked_where(l1qc>2,idp.variables[vars['L1Fe']][:])
l2qc = np.zeros((nstat,nsamp))
tmp = idp.variables[vars['L2QC']][:]
for ii in range(nstat):
for jj in range(nsamp):
l2qc[ii,jj]=np.double(tmp.data[ii,jj].tostring().decode("utf-8"))
#idp_l2 = nm.masked_where(np.logical_or(l2qc>2,depth>=critdepth),idp.variables[vars['L2Fe']][:])
idp_l2 = nm.masked_where(l2qc>2,idp.variables[vars['L2Fe']][:])
# Add L1 nd L2 for total and sort out common mask
idp_lt=nm.masked_where(np.logical_and(idp_l1.mask,idp_l2.mask),idp_l1+idp_l2)
# close the file
idp.close()
# Get basin masks
idp_mask=np.ones(np.shape(idp_lon))
idp_atlantic_mask, idp_pacific_mask, idp_indian_mask, idp_so_mask, idp_arctic_mask = oceanmasks(idp_lon,idp_lat,idp_mask)
idp_glob_mask=idp_atlantic_mask+idp_pacific_mask+idp_indian_mask+idp_so_mask
idp_basin_mask=idp_atlantic_mask+idp_pacific_mask+idp_indian_mask
fref=np.ones((1,3))
lref=np.ones((1,3))
fstd=np.ones((1,3))
lstd=np.ones((1,3))
# Southern Ocean Iron
fref[0,0]=nm.mean(
nm.masked_where(idp_dep>50,
nm.masked_where(idp_so_mask==0,idp_fe)))
fstd[0,0]=nm.std(
nm.masked_where(idp_dep>50,
nm.masked_where(idp_so_mask==0,idp_fe)))
# North Atlantic Iron
fref[0,1]=nm.mean(
nm.masked_where(idp_dep>50,
nm.masked_where(idp_basin_mask==0,
nm.masked_where(idp_so_mask==1,idp_fe))))
fstd[0,1]=nm.std(
nm.masked_where(idp_dep>50,
nm.masked_where(idp_basin_mask==0,
nm.masked_where(idp_so_mask==1,idp_fe))))
# Deep Ocean Iron
fref[0,2]=nm.mean(
nm.masked_where(idp_glob_mask==0,
nm.masked_where(idp_dep<=50,idp_fe)))
fstd[0,2]=nm.std(
nm.masked_where(idp_glob_mask==0,
nm.masked_where(idp_dep<=50,idp_fe)))
# Southern Ocean Ligand - Note data paucity in Southern Ocean, so extend search area
lref[0,0]=nm.mean(
nm.masked_where(idp_dep>50,
nm.masked_where(idp_lat>0,idp_lt)))
lstd[0,0]=nm.std(
nm.masked_where(idp_dep>50,
nm.masked_where(idp_lat>0,idp_lt)))
# North Atlantic Ligand
lref[0,1]=nm.mean(
nm.masked_where(idp_dep>50,
nm.masked_where(idp_basin_mask==0,
nm.masked_where(idp_so_mask==1,idp_lt))))
lstd[0,1]=nm.std(
nm.masked_where(idp_dep>50,
nm.masked_where(idp_basin_mask==0,
nm.masked_where(idp_so_mask==1,idp_lt))))
# Deep Ocean Ligand
lref[0,2]=nm.mean(
nm.masked_where(idp_glob_mask==0,
nm.masked_where(idp_dep<=50,idp_lt)))
lstd[0,2]=nm.std(
nm.masked_where(idp_glob_mask==0,
nm.masked_where(idp_dep<=50,idp_lt)))
except FileNotFoundError:
# Just use values from the paper
fref=np.array([[0.24949126, 0.28781143, 0.68981465]])
fstd=np.array([[0.43857741, 0.34694892, 2.31052641]])
lref=np.array([[1.87150518, 1.9718965 , 2.37402991]])
lstd=np.array([[1.0063749 , 0.89552883, 3.36445736]])
return fref, fstd, lref, lstd